{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "kR-4eNdK6lYS"
   },
   "source": [
    "Deep Learning\n",
    "=============\n",
    "\n",
    "Assignment 2\n",
    "------------\n",
    "\n",
    "Previously in `1_notmnist.ipynb`, we created a pickle with formatted datasets for training, development and testing on the [notMNIST dataset](http://yaroslavvb.blogspot.com/2011/09/notmnist-dataset.html).\n",
    "\n",
    "The goal of this assignment is to progressively train deeper and more accurate models using TensorFlow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "JLpLa8Jt7Vu4"
   },
   "outputs": [],
   "source": [
    "# These are all the modules we'll be using later. Make sure you can import them\n",
    "# before proceeding further.\n",
    "from __future__ import print_function\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from six.moves import cPickle as pickle\n",
    "from six.moves import range"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1HrCK6e17WzV"
   },
   "source": [
    "First reload the data we generated in `1_notmnist.ipynb`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "output_extras": [
      {
       "item_id": 1
      }
     ]
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 19456,
     "status": "ok",
     "timestamp": 1449847956073,
     "user": {
      "color": "",
      "displayName": "",
      "isAnonymous": false,
      "isMe": true,
      "permissionId": "",
      "photoUrl": "",
      "sessionId": "0",
      "userId": ""
     },
     "user_tz": 480
    },
    "id": "y3-cj1bpmuxc",
    "outputId": "0ddb1607-1fc4-4ddb-de28-6c7ab7fb0c33"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set (200000, 28, 28) (200000,)\n",
      "Validation set (10000, 28, 28) (10000,)\n",
      "Test set (10000, 28, 28) (10000,)\n"
     ]
    }
   ],
   "source": [
    "pickle_file = 'notMNIST.pickle'\n",
    "\n",
    "with open(pickle_file, 'rb') as f:\n",
    "    save = pickle.load(f)\n",
    "    train_dataset = save['train_dataset']\n",
    "    train_labels = save['train_labels']\n",
    "    valid_dataset = save['valid_dataset']\n",
    "    valid_labels = save['valid_labels']\n",
    "    test_dataset = save['test_dataset']\n",
    "    test_labels = save['test_labels']\n",
    "    del save  # hint to help gc free up memory\n",
    "    print('Training set', train_dataset.shape, train_labels.shape)\n",
    "    print('Validation set', valid_dataset.shape, valid_labels.shape)\n",
    "    print('Test set', test_dataset.shape, test_labels.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "L7aHrm6nGDMB"
   },
   "source": [
    "Reformat into a shape that's more adapted to the models we're going to train:\n",
    "- data as a flat matrix,\n",
    "- labels as float 1-hot encodings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "output_extras": [
      {
       "item_id": 1
      }
     ]
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 19723,
     "status": "ok",
     "timestamp": 1449847956364,
     "user": {
      "color": "",
      "displayName": "",
      "isAnonymous": false,
      "isMe": true,
      "permissionId": "",
      "photoUrl": "",
      "sessionId": "0",
      "userId": ""
     },
     "user_tz": 480
    },
    "id": "IRSyYiIIGIzS",
    "outputId": "2ba0fc75-1487-4ace-a562-cf81cae82793"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set (200000, 784) (200000, 10)\n",
      "Validation set (10000, 784) (10000, 10)\n",
      "Test set (10000, 784) (10000, 10)\n"
     ]
    }
   ],
   "source": [
    "image_size = 28\n",
    "num_labels = 10\n",
    "\n",
    "def reformat(dataset, labels):\n",
    "    dataset = dataset.reshape((-1, image_size * image_size)).astype(np.float32)\n",
    "    # Map 0 to [1.0, 0.0, 0.0 ...], 1 to [0.0, 1.0, 0.0 ...]\n",
    "    labels = (np.arange(num_labels) == labels[:,None]).astype(np.float32)\n",
    "    return dataset, labels\n",
    "train_dataset, train_labels = reformat(train_dataset, train_labels)\n",
    "valid_dataset, valid_labels = reformat(valid_dataset, valid_labels)\n",
    "test_dataset, test_labels = reformat(test_dataset, test_labels)\n",
    "print('Training set', train_dataset.shape, train_labels.shape)\n",
    "print('Validation set', valid_dataset.shape, valid_labels.shape)\n",
    "print('Test set', test_dataset.shape, test_labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.], dtype=float32)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nCLVqyQ5vPPH"
   },
   "source": [
    "We're first going to train a multinomial logistic regression (sigmoid regression) using simple gradient descent.\n",
    "\n",
    "TensorFlow works like this:\n",
    "* First you describe the computation that you want to see performed: what the inputs, the variables, and the operations look like. These get created as nodes over a computation graph. This description is all contained within the block below:\n",
    "\n",
    "      with graph.as_default():\n",
    "          ...\n",
    "\n",
    "* Then you can run the operations on this graph as many times as you want by calling `session.run()`, providing it outputs to fetch from the graph that get returned. This runtime operation is all contained in the block below:\n",
    "\n",
    "      with tf.Session(graph=graph) as session:\n",
    "          ...\n",
    "\n",
    "Let's load all the data into TensorFlow and build the computation graph corresponding to our training:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": false,
    "id": "Nfv39qvtvOl_"
   },
   "outputs": [],
   "source": [
    "# With gradient descent training, even this much data is prohibitive.\n",
    "# Subset the training data for faster turnaround.\n",
    "train_subset = 10000\n",
    "\n",
    "graph = tf.Graph()\n",
    "with graph.as_default():\n",
    "\n",
    "    # Input data.\n",
    "    # Load the training, validation and test data into constants that are\n",
    "    # attached to the graph.\n",
    "    tf_train_dataset = tf.constant(train_dataset[:train_subset, :])\n",
    "    tf_train_labels = tf.constant(train_labels[:train_subset])\n",
    "    tf_valid_dataset = tf.constant(valid_dataset)\n",
    "    tf_test_dataset = tf.constant(test_dataset)\n",
    "  \n",
    "    # Variables.\n",
    "    # These are the parameters that we are going to be training. The weight\n",
    "    # matrix will be initialized using random values following a (truncated)\n",
    "    # normal distribution. The biases get initialized to zero.\n",
    "    weights = tf.Variable(\n",
    "    tf.truncated_normal([image_size * image_size, num_labels]))\n",
    "    biases = tf.Variable(tf.zeros([num_labels]))\n",
    "  \n",
    "    # Training computation.\n",
    "    # We multiply the inputs with the weight matrix, and add biases. We compute\n",
    "    # the softmax and cross-entropy (it's one operation in TensorFlow, because\n",
    "    # it's very common, and it can be optimized). We take the average of this\n",
    "    # cross-entropy across all training examples: that's our loss.\n",
    "    logits = tf.matmul(tf_train_dataset, weights) + biases\n",
    "    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits, tf_train_labels))\n",
    "  \n",
    "    # Optimizer.\n",
    "    # We are going to find the minimum of this loss using gradient descent.\n",
    "    optimizer = tf.train.GradientDescentOptimizer(0.5).minimize(loss)\n",
    "  \n",
    "    # Predictions for the training, validation, and test data.\n",
    "    # These are not part of training, but merely here so that we can report\n",
    "    # accuracy figures as we train.\n",
    "    train_prediction = tf.nn.softmax(logits)\n",
    "    valid_prediction = tf.nn.softmax(tf.matmul(tf_valid_dataset, weights) + biases)\n",
    "    test_prediction = tf.nn.softmax(tf.matmul(tf_test_dataset, weights) + biases)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KQcL4uqISHjP"
   },
   "source": [
    "Let's run this computation and iterate:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "V_accur_list = [] #verification accuracy list\n",
    "step_list = [] # step number list\n",
    "cost_list = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "output_extras": [
      {
       "item_id": 9
      }
     ]
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 57454,
     "status": "ok",
     "timestamp": 1449847994134,
     "user": {
      "color": "",
      "displayName": "",
      "isAnonymous": false,
      "isMe": true,
      "permissionId": "",
      "photoUrl": "",
      "sessionId": "0",
      "userId": ""
     },
     "user_tz": 480
    },
    "id": "z2cjdenH869W",
    "outputId": "4c037ba1-b526-4d8e-e632-91e2a0333267"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialized\n",
      "Loss at step 0: 18.505116\n",
      "Training accuracy: 6.1%\n",
      "Validation accuracy: 7.4%\n",
      "Loss at step 10: 6.276209\n",
      "Training accuracy: 35.8%\n",
      "Validation accuracy: 38.0%\n",
      "Loss at step 20: 4.314642\n",
      "Training accuracy: 51.7%\n",
      "Validation accuracy: 51.9%\n",
      "Loss at step 30: 3.624284\n",
      "Training accuracy: 58.4%\n",
      "Validation accuracy: 59.3%\n",
      "Loss at step 40: 3.261755\n",
      "Training accuracy: 62.3%\n",
      "Validation accuracy: 63.0%\n",
      "Loss at step 50: 3.028780\n",
      "Training accuracy: 64.5%\n",
      "Validation accuracy: 65.3%\n",
      "Loss at step 60: 2.858864\n",
      "Training accuracy: 66.2%\n",
      "Validation accuracy: 67.1%\n",
      "Loss at step 70: 2.724869\n",
      "Training accuracy: 67.3%\n",
      "Validation accuracy: 68.2%\n",
      "Loss at step 80: 2.614150\n",
      "Training accuracy: 68.3%\n",
      "Validation accuracy: 69.0%\n",
      "Loss at step 90: 2.519850\n",
      "Training accuracy: 69.0%\n",
      "Validation accuracy: 69.6%\n",
      "Loss at step 100: 2.437702\n",
      "Training accuracy: 69.8%\n",
      "Validation accuracy: 70.1%\n",
      "Loss at step 110: 2.364886\n",
      "Training accuracy: 70.4%\n",
      "Validation accuracy: 70.4%\n",
      "Loss at step 120: 2.299477\n",
      "Training accuracy: 70.9%\n",
      "Validation accuracy: 70.8%\n",
      "Loss at step 130: 2.240110\n",
      "Training accuracy: 71.2%\n",
      "Validation accuracy: 71.1%\n",
      "Loss at step 140: 2.185786\n",
      "Training accuracy: 71.5%\n",
      "Validation accuracy: 71.4%\n",
      "Loss at step 150: 2.135742\n",
      "Training accuracy: 71.9%\n",
      "Validation accuracy: 71.5%\n",
      "Loss at step 160: 2.089381\n",
      "Training accuracy: 72.0%\n",
      "Validation accuracy: 71.8%\n",
      "Loss at step 170: 2.046233\n",
      "Training accuracy: 72.2%\n",
      "Validation accuracy: 72.0%\n",
      "Loss at step 180: 2.005905\n",
      "Training accuracy: 72.5%\n",
      "Validation accuracy: 72.2%\n",
      "Loss at step 190: 1.968069\n",
      "Training accuracy: 72.8%\n",
      "Validation accuracy: 72.3%\n",
      "Loss at step 200: 1.932453\n",
      "Training accuracy: 72.9%\n",
      "Validation accuracy: 72.5%\n",
      "Loss at step 210: 1.898822\n",
      "Training accuracy: 73.1%\n",
      "Validation accuracy: 72.6%\n",
      "Loss at step 220: 1.866974\n",
      "Training accuracy: 73.3%\n",
      "Validation accuracy: 72.7%\n",
      "Loss at step 230: 1.836738\n",
      "Training accuracy: 73.5%\n",
      "Validation accuracy: 72.9%\n",
      "Loss at step 240: 1.807970\n",
      "Training accuracy: 73.6%\n",
      "Validation accuracy: 73.0%\n",
      "Loss at step 250: 1.780543\n",
      "Training accuracy: 73.7%\n",
      "Validation accuracy: 73.1%\n",
      "Loss at step 260: 1.754350\n",
      "Training accuracy: 73.8%\n",
      "Validation accuracy: 73.2%\n",
      "Loss at step 270: 1.729294\n",
      "Training accuracy: 74.0%\n",
      "Validation accuracy: 73.3%\n",
      "Loss at step 280: 1.705289\n",
      "Training accuracy: 74.1%\n",
      "Validation accuracy: 73.3%\n",
      "Loss at step 290: 1.682261\n",
      "Training accuracy: 74.2%\n",
      "Validation accuracy: 73.4%\n",
      "Loss at step 300: 1.660144\n",
      "Training accuracy: 74.4%\n",
      "Validation accuracy: 73.5%\n",
      "Loss at step 310: 1.638876\n",
      "Training accuracy: 74.5%\n",
      "Validation accuracy: 73.5%\n",
      "Loss at step 320: 1.618406\n",
      "Training accuracy: 74.7%\n",
      "Validation accuracy: 73.5%\n",
      "Loss at step 330: 1.598680\n",
      "Training accuracy: 74.8%\n",
      "Validation accuracy: 73.5%\n",
      "Loss at step 340: 1.579658\n",
      "Training accuracy: 74.9%\n",
      "Validation accuracy: 73.5%\n",
      "Loss at step 350: 1.561298\n",
      "Training accuracy: 75.0%\n",
      "Validation accuracy: 73.5%\n",
      "Loss at step 360: 1.543563\n",
      "Training accuracy: 75.1%\n",
      "Validation accuracy: 73.5%\n",
      "Loss at step 370: 1.526420\n",
      "Training accuracy: 75.2%\n",
      "Validation accuracy: 73.6%\n",
      "Loss at step 380: 1.509837\n",
      "Training accuracy: 75.4%\n",
      "Validation accuracy: 73.7%\n",
      "Loss at step 390: 1.493786\n",
      "Training accuracy: 75.5%\n",
      "Validation accuracy: 73.6%\n",
      "Loss at step 400: 1.478238\n",
      "Training accuracy: 75.6%\n",
      "Validation accuracy: 73.6%\n",
      "Loss at step 410: 1.463170\n",
      "Training accuracy: 75.6%\n",
      "Validation accuracy: 73.5%\n",
      "Loss at step 420: 1.448559\n",
      "Training accuracy: 75.6%\n",
      "Validation accuracy: 73.6%\n",
      "Loss at step 430: 1.434384\n",
      "Training accuracy: 75.7%\n",
      "Validation accuracy: 73.6%\n",
      "Loss at step 440: 1.420623\n",
      "Training accuracy: 75.8%\n",
      "Validation accuracy: 73.6%\n",
      "Loss at step 450: 1.407259\n",
      "Training accuracy: 75.9%\n",
      "Validation accuracy: 73.6%\n",
      "Loss at step 460: 1.394275\n",
      "Training accuracy: 76.0%\n",
      "Validation accuracy: 73.6%\n",
      "Loss at step 470: 1.381655\n",
      "Training accuracy: 76.1%\n",
      "Validation accuracy: 73.7%\n",
      "Loss at step 480: 1.369380\n",
      "Training accuracy: 76.2%\n",
      "Validation accuracy: 73.7%\n",
      "Loss at step 490: 1.357443\n",
      "Training accuracy: 76.3%\n",
      "Validation accuracy: 73.6%\n",
      "Loss at step 500: 1.345823\n",
      "Training accuracy: 76.3%\n",
      "Validation accuracy: 73.6%\n",
      "Loss at step 510: 1.334511\n",
      "Training accuracy: 76.4%\n",
      "Validation accuracy: 73.7%\n",
      "Loss at step 520: 1.323492\n",
      "Training accuracy: 76.4%\n",
      "Validation accuracy: 73.7%\n",
      "Loss at step 530: 1.312759\n",
      "Training accuracy: 76.5%\n",
      "Validation accuracy: 73.7%\n",
      "Loss at step 540: 1.302296\n",
      "Training accuracy: 76.5%\n",
      "Validation accuracy: 73.8%\n",
      "Loss at step 550: 1.292094\n",
      "Training accuracy: 76.6%\n",
      "Validation accuracy: 73.8%\n",
      "Loss at step 560: 1.282142\n",
      "Training accuracy: 76.7%\n",
      "Validation accuracy: 73.8%\n",
      "Loss at step 570: 1.272429\n",
      "Training accuracy: 76.7%\n",
      "Validation accuracy: 73.9%\n",
      "Loss at step 580: 1.262947\n",
      "Training accuracy: 76.8%\n",
      "Validation accuracy: 73.9%\n",
      "Loss at step 590: 1.253691\n",
      "Training accuracy: 76.9%\n",
      "Validation accuracy: 73.9%\n",
      "Loss at step 600: 1.244645\n",
      "Training accuracy: 77.0%\n",
      "Validation accuracy: 74.0%\n",
      "Loss at step 610: 1.235804\n",
      "Training accuracy: 77.0%\n",
      "Validation accuracy: 74.0%\n",
      "Loss at step 620: 1.227160\n",
      "Training accuracy: 77.1%\n",
      "Validation accuracy: 74.0%\n",
      "Loss at step 630: 1.218707\n",
      "Training accuracy: 77.1%\n",
      "Validation accuracy: 74.1%\n",
      "Loss at step 640: 1.210435\n",
      "Training accuracy: 77.1%\n",
      "Validation accuracy: 74.1%\n",
      "Loss at step 650: 1.202339\n",
      "Training accuracy: 77.2%\n",
      "Validation accuracy: 74.1%\n",
      "Loss at step 660: 1.194411\n",
      "Training accuracy: 77.2%\n",
      "Validation accuracy: 74.2%\n",
      "Loss at step 670: 1.186647\n",
      "Training accuracy: 77.3%\n",
      "Validation accuracy: 74.1%\n",
      "Loss at step 680: 1.179039\n",
      "Training accuracy: 77.3%\n",
      "Validation accuracy: 74.2%\n",
      "Loss at step 690: 1.171583\n",
      "Training accuracy: 77.4%\n",
      "Validation accuracy: 74.2%\n",
      "Loss at step 700: 1.164273\n",
      "Training accuracy: 77.4%\n",
      "Validation accuracy: 74.3%\n",
      "Loss at step 710: 1.157104\n",
      "Training accuracy: 77.5%\n",
      "Validation accuracy: 74.2%\n",
      "Loss at step 720: 1.150071\n",
      "Training accuracy: 77.6%\n",
      "Validation accuracy: 74.2%\n",
      "Loss at step 730: 1.143168\n",
      "Training accuracy: 77.6%\n",
      "Validation accuracy: 74.3%\n",
      "Loss at step 740: 1.136393\n",
      "Training accuracy: 77.7%\n",
      "Validation accuracy: 74.3%\n",
      "Loss at step 750: 1.129741\n",
      "Training accuracy: 77.8%\n",
      "Validation accuracy: 74.4%\n",
      "Loss at step 760: 1.123207\n",
      "Training accuracy: 77.8%\n",
      "Validation accuracy: 74.4%\n",
      "Loss at step 770: 1.116788\n",
      "Training accuracy: 77.8%\n",
      "Validation accuracy: 74.4%\n",
      "Loss at step 780: 1.110482\n",
      "Training accuracy: 77.9%\n",
      "Validation accuracy: 74.4%\n",
      "Loss at step 790: 1.104283\n",
      "Training accuracy: 78.0%\n",
      "Validation accuracy: 74.4%\n",
      "Loss at step 800: 1.098187\n",
      "Training accuracy: 78.1%\n",
      "Validation accuracy: 74.5%\n",
      "Test accuracy: 82.1%\n"
     ]
    }
   ],
   "source": [
    "num_steps = 801\n",
    "\n",
    "def accuracy(predictions, labels):\n",
    "    return (100.0 * np.sum(np.argmax(predictions, 1) == np.argmax(labels, 1)) / predictions.shape[0])\n",
    "\n",
    "with tf.Session(graph=graph) as session:\n",
    "    # This is a one-time operation which ensures the parameters get initialized as\n",
    "    # we described in the graph: random weights for the matrix, zeros for the\n",
    "    # biases. \n",
    "    tf.initialize_all_variables().run()\n",
    "    print('Initialized')\n",
    "    for step in range(num_steps):\n",
    "        # Run the computations. We tell .run() that we want to run the optimizer,\n",
    "        # and get the loss value and the training predictions returned as numpy\n",
    "        # arrays.\n",
    "        _, l, predictions = session.run([optimizer, loss, train_prediction])\n",
    "        if (step % 10 == 0):\n",
    "            cost_list.append(l)\n",
    "            step_list.append(step)\n",
    "            print('Loss at step %d: %f' % (step, l))\n",
    "            print('Training accuracy: %.1f%%' % accuracy(\n",
    "            predictions, train_labels[:train_subset, :]))\n",
    "            # Calling .eval() on valid_prediction is basically like calling run(), but\n",
    "            # just to get that one numpy array. Note that it recomputes all its graph\n",
    "            # dependencies.\n",
    "            valid_accuracy = accuracy(valid_prediction.eval(), valid_labels)\n",
    "            V_accur_list.append(valid_accuracy)\n",
    "            print('Validation accuracy: %.1f%%' % valid_accuracy)\n",
    "    test_accuracy = accuracy(test_prediction.eval(), test_labels)\n",
    "    print('Test accuracy: %.1f%%' % test_accuracy)\n",
    "    #This line below allows for taking the predictions to a pandas dataframe and eventually a heatmap/confusion matrix\n",
    "    pred = test_prediction.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABrwAAAJpCAYAAAD/kJJdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x127f6d410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "fig, axes = plt.subplots(nrows = 1, ncols = 3, figsize = (30,9));\n",
    "fig.subplots_adjust(wspace=0.15, hspace=0) # Bringing subplots closer together. \n",
    "axes[0].plot(step_list, cost_list);\n",
    "axes[0].set_figure\n",
    "axes[0].set_title('Cost vs Steps', size = 20);\n",
    "axes[0].set_xlabel('Steps', size = 17);\n",
    "axes[0].set_ylabel('Cost', size = 17);\n",
    "axes[1].plot(step_list, V_accur_list);\n",
    "axes[1].set_title('Validation Accuracy vs Steps', size = 20);\n",
    "axes[1].set_xlabel('Steps', size = 17);\n",
    "axes[1].set_ylabel('Validation Accuracy', size = 17);\n",
    "\n",
    "# predictions\n",
    "predicted = [np.argmax(predictions[i,:], 0) for i in xrange(0,predictions.shape[0])]\n",
    "#actual\n",
    "t_labels = [np.where(test_labels[i] == 1)[0][0] for i in xrange(0,predictions.shape[0])]\n",
    "\n",
    "cm = metrics.confusion_matrix(t_labels, predicted)\n",
    "cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "confusion_matrix = pd.DataFrame(data = cm_normalized)\n",
    "sns.heatmap(confusion_matrix, annot=True, fmt=\".3f\", linewidths=.5, square = True,ax = axes[2], cmap = 'Blues_r', cbar = False);\n",
    "axes[2].set_ylabel('Actual label', size = 17);\n",
    "axes[2].set_xlabel('Predicted label', size = 17);\n",
    "confusion_title = 'Confusion Matrix ({}%)'.format(test_accuracy)\n",
    "axes[2].set_title(confusion_title, size = 20);\n",
    "fig.suptitle('Multinomial Logistic Regression', y=1,x = .52, size = 40);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "x68f-hxRGm3H"
   },
   "source": [
    "Let's now switch to stochastic gradient descent training instead, which is much faster.\n",
    "\n",
    "The graph will be similar, except that instead of holding all the training data into a constant node, we create a `Placeholder` node which will be fed actual data at every call of `session.run()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "qhPMzWYRGrzM"
   },
   "outputs": [],
   "source": [
    "batch_size = 128\n",
    "\n",
    "graph = tf.Graph()\n",
    "with graph.as_default():\n",
    "\n",
    "    # Input data. For the training data, we use a placeholder that will be fed\n",
    "    # at run time with a training minibatch.\n",
    "    tf_train_dataset = tf.placeholder(tf.float32,\n",
    "                                    shape=(batch_size, image_size * image_size))\n",
    "    tf_train_labels = tf.placeholder(tf.float32, shape=(batch_size, num_labels))\n",
    "    tf_valid_dataset = tf.constant(valid_dataset)\n",
    "    tf_test_dataset = tf.constant(test_dataset)\n",
    "  \n",
    "    # Variables.\n",
    "    weights = tf.Variable(tf.truncated_normal([image_size * image_size, num_labels]))\n",
    "    biases = tf.Variable(tf.zeros([num_labels]))\n",
    "  \n",
    "    # Training computation.\n",
    "    logits = tf.matmul(tf_train_dataset, weights) + biases\n",
    "    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits, tf_train_labels))\n",
    "  \n",
    "    # Optimizer.\n",
    "    optimizer = tf.train.GradientDescentOptimizer(0.5).minimize(loss)\n",
    "  \n",
    "    # Predictions for the training, validation, and test data.\n",
    "    train_prediction = tf.nn.softmax(logits)\n",
    "    valid_prediction = tf.nn.softmax(tf.matmul(tf_valid_dataset, weights) + biases)\n",
    "    test_prediction = tf.nn.softmax(tf.matmul(tf_test_dataset, weights) + biases)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "XmVZESmtG4JH"
   },
   "source": [
    "Let's run it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "output_extras": [
      {
       "item_id": 6
      }
     ]
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 66292,
     "status": "ok",
     "timestamp": 1449848003013,
     "user": {
      "color": "",
      "displayName": "",
      "isAnonymous": false,
      "isMe": true,
      "permissionId": "",
      "photoUrl": "",
      "sessionId": "0",
      "userId": ""
     },
     "user_tz": 480
    },
    "id": "FoF91pknG_YW",
    "outputId": "d255c80e-954d-4183-ca1c-c7333ce91d0a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialized\n",
      "Minibatch loss at step 0: 18.513424\n",
      "Minibatch accuracy: 8.6%\n",
      "Validation accuracy: 13.1%\n",
      "Minibatch loss at step 500: 2.576057\n",
      "Minibatch accuracy: 72.7%\n",
      "Validation accuracy: 75.5%\n",
      "Minibatch loss at step 1000: 1.237476\n",
      "Minibatch accuracy: 75.0%\n",
      "Validation accuracy: 76.6%\n",
      "Minibatch loss at step 1500: 1.269744\n",
      "Minibatch accuracy: 72.7%\n",
      "Validation accuracy: 77.9%\n",
      "Minibatch loss at step 2000: 1.193723\n",
      "Minibatch accuracy: 78.1%\n",
      "Validation accuracy: 76.4%\n",
      "Minibatch loss at step 2500: 1.189279\n",
      "Minibatch accuracy: 74.2%\n",
      "Validation accuracy: 78.2%\n",
      "Minibatch loss at step 3000: 0.867406\n",
      "Minibatch accuracy: 74.2%\n",
      "Validation accuracy: 78.9%\n",
      "Minibatch loss at step 3500: 0.656800\n",
      "Minibatch accuracy: 80.5%\n",
      "Validation accuracy: 78.7%\n",
      "Minibatch loss at step 4000: 1.019501\n",
      "Minibatch accuracy: 75.0%\n",
      "Validation accuracy: 79.5%\n",
      "Minibatch loss at step 4500: 0.670508\n",
      "Minibatch accuracy: 81.2%\n",
      "Validation accuracy: 79.6%\n",
      "Minibatch loss at step 5000: 0.682256\n",
      "Minibatch accuracy: 83.6%\n",
      "Validation accuracy: 79.5%\n",
      "Test accuracy: 87.3%\n"
     ]
    }
   ],
   "source": [
    "num_steps = 5001\n",
    "\n",
    "with tf.Session(graph=graph) as session:\n",
    "    tf.initialize_all_variables().run()\n",
    "    print(\"Initialized\")\n",
    "    for step in range(num_steps):\n",
    "        # Pick an offset within the training data, which has been randomized.\n",
    "        # Note: we could use better randomization across epochs.\n",
    "        offset = (step * batch_size) % (train_labels.shape[0] - batch_size)\n",
    "        # Generate a minibatch.\n",
    "        batch_data = train_dataset[offset:(offset + batch_size), :]\n",
    "        batch_labels = train_labels[offset:(offset + batch_size), :]\n",
    "        # Prepare a dictionary telling the session where to feed the minibatch.\n",
    "        # The key of the dictionary is the placeholder node of the graph to be fed,\n",
    "        # and the value is the numpy array to feed to it.\n",
    "        feed_dict = {tf_train_dataset : batch_data, tf_train_labels : batch_labels}\n",
    "        _, l, predictions = session.run(\n",
    "          [optimizer, loss, train_prediction], feed_dict=feed_dict)\n",
    "        if (step % 500 == 0):\n",
    "            print(\"Minibatch loss at step %d: %f\" % (step, l))\n",
    "            print(\"Minibatch accuracy: %.1f%%\" % accuracy(predictions, batch_labels))\n",
    "            print(\"Validation accuracy: %.1f%%\" % accuracy(valid_prediction.eval(), valid_labels))\n",
    "    print(\"Test accuracy: %.1f%%\" % accuracy(test_prediction.eval(), test_labels))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7omWxtvLLxik"
   },
   "source": [
    "---\n",
    "Problem\n",
    "-------\n",
    "\n",
    "Turn the logistic regression example with SGD into a 1-hidden layer neural network with rectified linear units [nn.relu()](https://www.tensorflow.org/versions/r0.7/api_docs/python/nn.html#relu) and 1024 hidden nodes. This model should improve your validation / test accuracy.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "V_accur_list, step_list, cost_list = [], [], []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Setting up the Computation graph always comes first\n",
    "\n",
    "batch_size = 128\n",
    "n_hidden_nodes = 1024\n",
    "image_size = 28\n",
    "\n",
    "graph = tf.Graph()\n",
    "with graph.as_default():\n",
    "    \n",
    "    # Input data. For the training data, we use a placeholder that will be \n",
    "    # fed at runtime with a training minibatch\n",
    "    tf_train_dataset = tf.placeholder(tf.float32,\n",
    "                                    shape=(batch_size, image_size * image_size))\n",
    "    tf_train_labels = tf.placeholder(tf.float32, shape=(batch_size, num_labels))\n",
    "    tf_valid_dataset = tf.constant(valid_dataset)\n",
    "    tf_test_dataset = tf.constant(test_dataset)\n",
    "    \n",
    "    # Variables.\n",
    "    weights_01 = tf.Variable(\n",
    "    tf.truncated_normal([image_size * image_size, n_hidden_nodes]))\n",
    "    weights_12 = tf.Variable(tf.truncated_normal([n_hidden_nodes, num_labels]))\n",
    "    biases_01 = tf.Variable(tf.zeros([n_hidden_nodes]))\n",
    "    biases_12 = tf.Variable(tf.zeros([num_labels]))\n",
    "\n",
    "    # Training computation.\n",
    "    z_01= tf.matmul(tf_train_dataset, weights_01) + biases_01\n",
    "    h1 = tf.nn.relu(z_01)\n",
    "    z_12 = tf.matmul(h1, weights_12) + biases_12\n",
    "    loss = tf.reduce_mean(\n",
    "    tf.nn.softmax_cross_entropy_with_logits(z_12, tf_train_labels))\n",
    "\n",
    "    # Optimizer.\n",
    "    optimizer = tf.train.GradientDescentOptimizer(0.5).minimize(loss)\n",
    "\n",
    "    # Predictions for the training, validation, and test data.\n",
    "    train_prediction = tf.nn.softmax(z_12)\n",
    "    valid_prediction = tf.nn.softmax(\n",
    "    tf.matmul(tf.nn.relu(tf.matmul(tf_valid_dataset, weights_01) + biases_01), weights_12) + biases_12)\n",
    "    test_prediction = tf.nn.softmax(tf.matmul(tf.nn.relu(tf.matmul(tf_test_dataset, weights_01) + biases_01), weights_12) + biases_12) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialized\n",
      "Minibatch loss at step 0: 333.651062\n",
      "Minibatch accuracy: 7.8%\n",
      "Validation accuracy: 32.7%\n",
      "Minibatch loss at step 100: 40.900742\n",
      "Minibatch accuracy: 75.8%\n",
      "Validation accuracy: 77.9%\n",
      "Minibatch loss at step 200: 58.991444\n",
      "Minibatch accuracy: 73.4%\n",
      "Validation accuracy: 79.4%\n",
      "Minibatch loss at step 300: 26.388268\n",
      "Minibatch accuracy: 82.0%\n",
      "Validation accuracy: 77.3%\n",
      "Minibatch loss at step 400: 11.613901\n",
      "Minibatch accuracy: 85.2%\n",
      "Validation accuracy: 78.4%\n",
      "Minibatch loss at step 500: 17.615753\n",
      "Minibatch accuracy: 77.3%\n",
      "Validation accuracy: 81.9%\n",
      "Minibatch loss at step 600: 4.695144\n",
      "Minibatch accuracy: 89.8%\n",
      "Validation accuracy: 81.4%\n",
      "Minibatch loss at step 700: 8.331864\n",
      "Minibatch accuracy: 83.6%\n",
      "Validation accuracy: 81.1%\n",
      "Minibatch loss at step 800: 6.983974\n",
      "Minibatch accuracy: 82.8%\n",
      "Validation accuracy: 80.8%\n",
      "Minibatch loss at step 900: 10.730562\n",
      "Minibatch accuracy: 75.0%\n",
      "Validation accuracy: 79.3%\n",
      "Minibatch loss at step 1000: 9.588756\n",
      "Minibatch accuracy: 77.3%\n",
      "Validation accuracy: 81.7%\n",
      "Minibatch loss at step 1100: 7.534806\n",
      "Minibatch accuracy: 78.1%\n",
      "Validation accuracy: 80.9%\n",
      "Minibatch loss at step 1200: 2.764992\n",
      "Minibatch accuracy: 87.5%\n",
      "Validation accuracy: 81.6%\n",
      "Minibatch loss at step 1300: 14.639361\n",
      "Minibatch accuracy: 79.7%\n",
      "Validation accuracy: 81.5%\n",
      "Minibatch loss at step 1400: 9.409963\n",
      "Minibatch accuracy: 78.1%\n",
      "Validation accuracy: 77.6%\n",
      "Minibatch loss at step 1500: 8.250807\n",
      "Minibatch accuracy: 75.0%\n",
      "Validation accuracy: 80.7%\n",
      "Minibatch loss at step 1600: 4.903131\n",
      "Minibatch accuracy: 75.8%\n",
      "Validation accuracy: 81.8%\n",
      "Minibatch loss at step 1700: 3.089653\n",
      "Minibatch accuracy: 87.5%\n",
      "Validation accuracy: 82.4%\n",
      "Minibatch loss at step 1800: 7.198960\n",
      "Minibatch accuracy: 85.9%\n",
      "Validation accuracy: 81.8%\n",
      "Minibatch loss at step 1900: 6.854327\n",
      "Minibatch accuracy: 82.8%\n",
      "Validation accuracy: 79.3%\n",
      "Minibatch loss at step 2000: 6.701708\n",
      "Minibatch accuracy: 83.6%\n",
      "Validation accuracy: 80.6%\n",
      "Minibatch loss at step 2100: 3.699832\n",
      "Minibatch accuracy: 88.3%\n",
      "Validation accuracy: 81.6%\n",
      "Minibatch loss at step 2200: 9.038855\n",
      "Minibatch accuracy: 81.2%\n",
      "Validation accuracy: 78.5%\n",
      "Minibatch loss at step 2300: 3.582011\n",
      "Minibatch accuracy: 77.3%\n",
      "Validation accuracy: 82.7%\n",
      "Minibatch loss at step 2400: 4.922134\n",
      "Minibatch accuracy: 80.5%\n",
      "Validation accuracy: 81.8%\n",
      "Minibatch loss at step 2500: 9.794471\n",
      "Minibatch accuracy: 81.2%\n",
      "Validation accuracy: 81.3%\n",
      "Minibatch loss at step 2600: 4.787688\n",
      "Minibatch accuracy: 87.5%\n",
      "Validation accuracy: 82.3%\n",
      "Minibatch loss at step 2700: 3.416420\n",
      "Minibatch accuracy: 78.9%\n",
      "Validation accuracy: 82.3%\n",
      "Minibatch loss at step 2800: 4.294723\n",
      "Minibatch accuracy: 82.8%\n",
      "Validation accuracy: 81.9%\n",
      "Minibatch loss at step 2900: 4.669366\n",
      "Minibatch accuracy: 86.7%\n",
      "Validation accuracy: 82.4%\n",
      "Minibatch loss at step 3000: 2.953568\n",
      "Minibatch accuracy: 82.0%\n",
      "Validation accuracy: 82.3%\n",
      "Minibatch loss at step 3100: 4.666967\n",
      "Minibatch accuracy: 78.9%\n",
      "Validation accuracy: 81.7%\n",
      "Minibatch loss at step 3200: 1.326057\n",
      "Minibatch accuracy: 88.3%\n",
      "Validation accuracy: 82.8%\n",
      "Minibatch loss at step 3300: 2.119037\n",
      "Minibatch accuracy: 89.1%\n",
      "Validation accuracy: 82.1%\n",
      "Minibatch loss at step 3400: 4.758450\n",
      "Minibatch accuracy: 82.8%\n",
      "Validation accuracy: 82.5%\n",
      "Minibatch loss at step 3500: 2.748294\n",
      "Minibatch accuracy: 89.1%\n",
      "Validation accuracy: 80.1%\n",
      "Minibatch loss at step 3600: 1.408967\n",
      "Minibatch accuracy: 89.1%\n",
      "Validation accuracy: 83.6%\n",
      "Minibatch loss at step 3700: 5.352250\n",
      "Minibatch accuracy: 83.6%\n",
      "Validation accuracy: 81.7%\n",
      "Minibatch loss at step 3800: 1.979409\n",
      "Minibatch accuracy: 85.9%\n",
      "Validation accuracy: 83.6%\n",
      "Minibatch loss at step 3900: 2.654440\n",
      "Minibatch accuracy: 83.6%\n",
      "Validation accuracy: 82.7%\n",
      "Minibatch loss at step 4000: 1.983046\n",
      "Minibatch accuracy: 86.7%\n",
      "Validation accuracy: 81.9%\n",
      "Minibatch loss at step 4100: 4.785589\n",
      "Minibatch accuracy: 81.2%\n",
      "Validation accuracy: 82.6%\n",
      "Minibatch loss at step 4200: 1.161876\n",
      "Minibatch accuracy: 89.1%\n",
      "Validation accuracy: 83.2%\n",
      "Minibatch loss at step 4300: 5.787623\n",
      "Minibatch accuracy: 78.1%\n",
      "Validation accuracy: 83.4%\n",
      "Minibatch loss at step 4400: 1.948777\n",
      "Minibatch accuracy: 83.6%\n",
      "Validation accuracy: 82.9%\n",
      "Minibatch loss at step 4500: 0.644479\n",
      "Minibatch accuracy: 89.1%\n",
      "Validation accuracy: 83.3%\n",
      "Minibatch loss at step 4600: 4.143356\n",
      "Minibatch accuracy: 81.2%\n",
      "Validation accuracy: 81.6%\n",
      "Minibatch loss at step 4700: 2.180049\n",
      "Minibatch accuracy: 88.3%\n",
      "Validation accuracy: 82.6%\n",
      "Minibatch loss at step 4800: 2.342604\n",
      "Minibatch accuracy: 89.1%\n",
      "Validation accuracy: 83.5%\n",
      "Minibatch loss at step 4900: 1.741131\n",
      "Minibatch accuracy: 84.4%\n",
      "Validation accuracy: 83.0%\n",
      "Minibatch loss at step 5000: 1.046626\n",
      "Minibatch accuracy: 86.7%\n",
      "Validation accuracy: 82.8%\n",
      "Minibatch loss at step 5100: 1.820904\n",
      "Minibatch accuracy: 85.2%\n",
      "Validation accuracy: 83.8%\n",
      "Minibatch loss at step 5200: 1.633735\n",
      "Minibatch accuracy: 82.0%\n",
      "Validation accuracy: 83.3%\n",
      "Minibatch loss at step 5300: 1.188792\n",
      "Minibatch accuracy: 83.6%\n",
      "Validation accuracy: 83.8%\n",
      "Minibatch loss at step 5400: 1.960924\n",
      "Minibatch accuracy: 86.7%\n",
      "Validation accuracy: 82.6%\n",
      "Minibatch loss at step 5500: 1.306462\n",
      "Minibatch accuracy: 82.8%\n",
      "Validation accuracy: 83.1%\n",
      "Minibatch loss at step 5600: 1.800799\n",
      "Minibatch accuracy: 80.5%\n",
      "Validation accuracy: 82.6%\n",
      "Minibatch loss at step 5700: 1.345218\n",
      "Minibatch accuracy: 87.5%\n",
      "Validation accuracy: 83.0%\n",
      "Minibatch loss at step 5800: 3.524932\n",
      "Minibatch accuracy: 85.9%\n",
      "Validation accuracy: 83.0%\n",
      "Minibatch loss at step 5900: 2.519739\n",
      "Minibatch accuracy: 89.1%\n",
      "Validation accuracy: 83.6%\n",
      "Minibatch loss at step 6000: 3.615945\n",
      "Minibatch accuracy: 86.7%\n",
      "Validation accuracy: 83.0%\n",
      "Minibatch loss at step 6100: 1.665506\n",
      "Minibatch accuracy: 88.3%\n",
      "Validation accuracy: 83.9%\n",
      "Minibatch loss at step 6200: 4.853050\n",
      "Minibatch accuracy: 82.8%\n",
      "Validation accuracy: 82.4%\n",
      "Minibatch loss at step 6300: 1.700856\n",
      "Minibatch accuracy: 85.2%\n",
      "Validation accuracy: 83.5%\n",
      "Minibatch loss at step 6400: 1.352160\n",
      "Minibatch accuracy: 89.1%\n",
      "Validation accuracy: 83.7%\n",
      "Minibatch loss at step 6500: 2.679376\n",
      "Minibatch accuracy: 76.6%\n",
      "Validation accuracy: 83.2%\n",
      "Minibatch loss at step 6600: 2.472259\n",
      "Minibatch accuracy: 85.2%\n",
      "Validation accuracy: 83.5%\n",
      "Minibatch loss at step 6700: 0.623309\n",
      "Minibatch accuracy: 89.1%\n",
      "Validation accuracy: 83.8%\n",
      "Minibatch loss at step 6800: 1.187265\n",
      "Minibatch accuracy: 89.1%\n",
      "Validation accuracy: 84.3%\n",
      "Minibatch loss at step 6900: 1.783331\n",
      "Minibatch accuracy: 83.6%\n",
      "Validation accuracy: 83.7%\n",
      "Minibatch loss at step 7000: 1.616698\n",
      "Minibatch accuracy: 85.2%\n",
      "Validation accuracy: 83.9%\n",
      "Minibatch loss at step 7100: 2.334317\n",
      "Minibatch accuracy: 82.0%\n",
      "Validation accuracy: 83.3%\n",
      "Minibatch loss at step 7200: 2.380092\n",
      "Minibatch accuracy: 88.3%\n",
      "Validation accuracy: 82.7%\n",
      "Minibatch loss at step 7300: 2.781913\n",
      "Minibatch accuracy: 89.1%\n",
      "Validation accuracy: 83.7%\n",
      "Minibatch loss at step 7400: 2.036647\n",
      "Minibatch accuracy: 87.5%\n",
      "Validation accuracy: 84.0%\n",
      "Minibatch loss at step 7500: 1.499790\n",
      "Minibatch accuracy: 86.7%\n",
      "Validation accuracy: 83.7%\n",
      "Minibatch loss at step 7600: 1.004812\n",
      "Minibatch accuracy: 89.1%\n",
      "Validation accuracy: 84.1%\n",
      "Minibatch loss at step 7700: 0.708594\n",
      "Minibatch accuracy: 91.4%\n",
      "Validation accuracy: 83.9%\n",
      "Minibatch loss at step 7800: 1.516634\n",
      "Minibatch accuracy: 84.4%\n",
      "Validation accuracy: 83.8%\n",
      "Minibatch loss at step 7900: 1.728483\n",
      "Minibatch accuracy: 83.6%\n",
      "Validation accuracy: 83.7%\n",
      "Minibatch loss at step 8000: 1.058906\n",
      "Minibatch accuracy: 91.4%\n",
      "Validation accuracy: 83.8%\n",
      "Minibatch loss at step 8100: 1.610225\n",
      "Minibatch accuracy: 89.1%\n",
      "Validation accuracy: 83.3%\n",
      "Minibatch loss at step 8200: 0.511389\n",
      "Minibatch accuracy: 90.6%\n",
      "Validation accuracy: 83.4%\n",
      "Minibatch loss at step 8300: 0.222858\n",
      "Minibatch accuracy: 94.5%\n",
      "Validation accuracy: 84.2%\n",
      "Minibatch loss at step 8400: 0.583423\n",
      "Minibatch accuracy: 91.4%\n",
      "Validation accuracy: 84.7%\n",
      "Minibatch loss at step 8500: 0.484817\n",
      "Minibatch accuracy: 91.4%\n",
      "Validation accuracy: 84.0%\n",
      "Minibatch loss at step 8600: 0.678576\n",
      "Minibatch accuracy: 89.8%\n",
      "Validation accuracy: 84.1%\n",
      "Minibatch loss at step 8700: 0.909199\n",
      "Minibatch accuracy: 85.2%\n",
      "Validation accuracy: 83.9%\n",
      "Minibatch loss at step 8800: 0.619312\n",
      "Minibatch accuracy: 84.4%\n",
      "Validation accuracy: 83.9%\n",
      "Minibatch loss at step 8900: 0.487721\n",
      "Minibatch accuracy: 90.6%\n",
      "Validation accuracy: 83.8%\n",
      "Minibatch loss at step 9000: 1.224843\n",
      "Minibatch accuracy: 85.9%\n",
      "Validation accuracy: 84.0%\n",
      "Minibatch loss at step 9100: 0.821212\n",
      "Minibatch accuracy: 89.1%\n",
      "Validation accuracy: 83.5%\n",
      "Minibatch loss at step 9200: 0.932114\n",
      "Minibatch accuracy: 87.5%\n",
      "Validation accuracy: 83.6%\n",
      "Minibatch loss at step 9300: 2.070440\n",
      "Minibatch accuracy: 85.9%\n",
      "Validation accuracy: 84.1%\n",
      "Minibatch loss at step 9400: 0.607912\n",
      "Minibatch accuracy: 88.3%\n",
      "Validation accuracy: 83.3%\n",
      "Minibatch loss at step 9500: 0.848129\n",
      "Minibatch accuracy: 85.2%\n",
      "Validation accuracy: 84.0%\n",
      "Minibatch loss at step 9600: 0.677778\n",
      "Minibatch accuracy: 86.7%\n",
      "Validation accuracy: 83.6%\n",
      "Minibatch loss at step 9700: 1.408764\n",
      "Minibatch accuracy: 91.4%\n",
      "Validation accuracy: 83.9%\n",
      "Minibatch loss at step 9800: 0.683917\n",
      "Minibatch accuracy: 89.1%\n",
      "Validation accuracy: 84.1%\n",
      "Minibatch loss at step 9900: 1.439012\n",
      "Minibatch accuracy: 84.4%\n",
      "Validation accuracy: 84.0%\n",
      "Minibatch loss at step 10000: 1.904918\n",
      "Minibatch accuracy: 85.9%\n",
      "Validation accuracy: 84.0%\n",
      "Minibatch loss at step 10100: 0.666415\n",
      "Minibatch accuracy: 90.6%\n",
      "Validation accuracy: 84.1%\n",
      "Minibatch loss at step 10200: 1.579196\n",
      "Minibatch accuracy: 93.0%\n",
      "Validation accuracy: 84.8%\n",
      "Minibatch loss at step 10300: 0.554305\n",
      "Minibatch accuracy: 89.1%\n",
      "Validation accuracy: 84.5%\n",
      "Minibatch loss at step 10400: 3.629221\n",
      "Minibatch accuracy: 80.5%\n",
      "Validation accuracy: 84.1%\n",
      "Minibatch loss at step 10500: 1.557206\n",
      "Minibatch accuracy: 82.0%\n",
      "Validation accuracy: 84.1%\n",
      "Minibatch loss at step 10600: 1.165371\n",
      "Minibatch accuracy: 90.6%\n",
      "Validation accuracy: 83.2%\n",
      "Minibatch loss at step 10700: 1.240606\n",
      "Minibatch accuracy: 89.1%\n",
      "Validation accuracy: 84.8%\n",
      "Minibatch loss at step 10800: 1.100813\n",
      "Minibatch accuracy: 86.7%\n",
      "Validation accuracy: 84.7%\n",
      "Minibatch loss at step 10900: 0.915508\n",
      "Minibatch accuracy: 89.1%\n",
      "Validation accuracy: 84.7%\n",
      "Minibatch loss at step 11000: 0.830617\n",
      "Minibatch accuracy: 92.2%\n",
      "Validation accuracy: 84.1%\n",
      "Minibatch loss at step 11100: 0.981888\n",
      "Minibatch accuracy: 85.2%\n",
      "Validation accuracy: 84.4%\n",
      "Minibatch loss at step 11200: 1.215971\n",
      "Minibatch accuracy: 88.3%\n",
      "Validation accuracy: 84.6%\n",
      "Minibatch loss at step 11300: 0.263222\n",
      "Minibatch accuracy: 89.8%\n",
      "Validation accuracy: 85.0%\n",
      "Minibatch loss at step 11400: 1.057466\n",
      "Minibatch accuracy: 88.3%\n",
      "Validation accuracy: 84.5%\n",
      "Minibatch loss at step 11500: 0.486742\n",
      "Minibatch accuracy: 91.4%\n",
      "Validation accuracy: 84.9%\n",
      "Minibatch loss at step 11600: 0.919729\n",
      "Minibatch accuracy: 87.5%\n",
      "Validation accuracy: 84.9%\n",
      "Minibatch loss at step 11700: 0.402324\n",
      "Minibatch accuracy: 89.8%\n",
      "Validation accuracy: 84.8%\n",
      "Minibatch loss at step 11800: 1.191757\n",
      "Minibatch accuracy: 89.1%\n",
      "Validation accuracy: 84.7%\n",
      "Minibatch loss at step 11900: 2.240254\n",
      "Minibatch accuracy: 88.3%\n",
      "Validation accuracy: 85.3%\n",
      "Minibatch loss at step 12000: 1.661023\n",
      "Minibatch accuracy: 86.7%\n",
      "Validation accuracy: 85.0%\n",
      "Test accuracy: 91.0%\n"
     ]
    }
   ],
   "source": [
    "num_steps = 12001\n",
    "\n",
    "with tf.Session(graph=graph) as session:\n",
    "    tf.initialize_all_variables().run()\n",
    "    print(\"Initialized\")\n",
    "    for step in range(num_steps):\n",
    "        # Pick an offset within the training data, which has been randomized.\n",
    "        # Note: we could use better randomization across epochs.\n",
    "        offset = (step * batch_size) % (train_labels.shape[0] - batch_size)\n",
    "        # Generate a minibatch.\n",
    "        batch_data = train_dataset[offset:(offset + batch_size), :]\n",
    "        batch_labels = train_labels[offset:(offset + batch_size), :]\n",
    "        # Prepare a dictionary telling the session where to feed the minibatch.\n",
    "        # The key of the dictionary is the placeholder node of the graph to be fed,\n",
    "        # and the value is the numpy array to feed to it.\n",
    "        feed_dict = {tf_train_dataset : batch_data, tf_train_labels : batch_labels}\n",
    "        _, l, predictions = session.run(\n",
    "          [optimizer, loss, train_prediction], feed_dict=feed_dict)\n",
    "        if (step % 100 == 0):\n",
    "            cost_list.append(l)\n",
    "            step_list.append(step)\n",
    "            print(\"Minibatch loss at step %d: %f\" % (step, l))\n",
    "            print(\"Minibatch accuracy: %.1f%%\" % accuracy(predictions, batch_labels))\n",
    "            valid_accuracy = accuracy(valid_prediction.eval(), valid_labels)\n",
    "            V_accur_list.append(valid_accuracy)\n",
    "            print(\"Validation accuracy: %.1f%%\" % valid_accuracy)\n",
    "    test_accuracy = accuracy(test_prediction.eval(), test_labels)\n",
    "    print('Test accuracy: %.1f%%' % test_accuracy)\n",
    "    #This line below allows for taking the predictions to a pandas dataframe and eventually a heatmap/confusion matrix\n",
    "    pred = test_prediction.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABsEAAAJpCAYAAAD8J5x1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1365a0b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows = 1, ncols = 3, figsize = (30,9));\n",
    "fig.subplots_adjust(wspace=0.15, hspace=0) # Bringing subplots closer together. \n",
    "axes[0].plot(step_list, cost_list);\n",
    "axes[0].set_figure\n",
    "axes[0].set_title('Cost vs Steps', size = 20);\n",
    "axes[0].set_xlabel('Steps', size = 17);\n",
    "axes[0].set_ylabel('Cost', size = 17);\n",
    "axes[1].plot(step_list, V_accur_list);\n",
    "axes[1].set_title('Validation Accuracy vs Steps', size = 20);\n",
    "axes[1].set_xlabel('Steps', size = 17);\n",
    "axes[1].set_ylabel('Validation Accuracy', size = 17);\n",
    "\n",
    "# predictions\n",
    "predicted = [np.argmax(pred[i,:], 0) for i in xrange(0,pred.shape[0])]\n",
    "#actual\n",
    "t_labels = [np.where(test_labels[i] == 1)[0][0] for i in xrange(0,pred.shape[0])]\n",
    "\n",
    "cm = metrics.confusion_matrix(t_labels, predicted)\n",
    "cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "confusion_matrix = pd.DataFrame(data = cm_normalized)\n",
    "sns.heatmap(confusion_matrix, annot=True, fmt=\".3f\", linewidths=.5, square = True,ax = axes[2], cmap = 'Blues_r', cbar = False);\n",
    "axes[2].set_ylabel('Actual label', size = 17);\n",
    "axes[2].set_xlabel('Predicted label', size = 17);\n",
    "confusion_title = 'Confusion Matrix ({}%)'.format(test_accuracy)\n",
    "axes[2].set_title(confusion_title, size = 20);\n",
    "fig.suptitle('1-Hidden Layer Neural Network', y=1,x = .52, size = 40);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "default_view": {},
   "name": "2_fullyconnected.ipynb",
   "provenance": [],
   "version": "0.3.2",
   "views": {}
  },
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
